Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

BACKGROUND

Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet.

METHODS

aSAH patients from 2 prospective cohorts (Zurich-derivation cohort, Aachen-validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1-4 vs. 5-8) or ordinal outcome (GOSE-extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination.

RESULTS

A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity.

CONCLUSIONS

MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

Functional connectivity and complexity analyses of resting-state fMRI in pre-adolescents demonstrating the behavioral symptoms of ADHD

Attention deficit hyperactivity disorder (ADHD) has been characterized by impairments among distributed functional brain networks, e.g., the frontoparietal network (FPN), default mode network (DMN), reward and motivation-related circuits (RMN), and salience network (SAL). In the current study, we evaluated the complexity and functional connectivity (FC) of resting state fMRI (rsfMRI) in pre-adolescents with the behavioral symptoms of ADHD, for pathology-relevant networks. We leveraged data from the Adolescent Brain and Cognitive Development (ABCD) Study. The final study sample included 63 children demonstrating the behavioral features of ADHD and 92 healthy control children matched on age, sex, and pubertal development status. For selected regions in the relevant networks, ANCOVA compared multiscale entropy (MSE) and FC between the groups. Finally, differences in the association between MSE and FC were evaluated. We found significantly reduced MSE along with increased FC within the FPN of pre-adolescents demonstrating the behavior symptoms of ADHD compared to matched healthy controls. Significant partial correlations between MSE and FC emerged in the FPN and RMN in the healthy controls however the association was absent in the participants demonstrating the behavior symptoms of ADHD. The current findings of complexity and FC in ADHD pathology support hypotheses of altered function of inhibitory control networks in ADHD.

Multiscale entropy in a 10-minute vigilance task

Research has shown multiscale entropy, brain signal behavior across time scales, to reliably increase at lower time scales with time-on-task fatigue. However, multiscale entropy has not been examined in short vigilance tasks (i.e., ≤ 10 min). Addressing this gap, we examine multiscale entropy during a 10-minute Psychomotor Vigilance Test (PVT). Thirty-four participants provided neural data while completing the PVT. We compared the first 2 min of the task to the 7th and 8th minutes to avoid end-spurt effects. Results suggested increased multiscale entropy at lower time scales later compared to earlier in the task, suggesting multiscale entropy is a strong marker of time-on-task fatigue onset during short vigils. Separate analyses for Fast and Slow performers reveal differential entropy patterns, particularly over visual cortices. Here, observed brain-behavior linkage between entropy and reaction time for slow performers suggests that entropy assays over sensory cortices might have predictive value for fatigue onset or shifts from on- to off-task states.

Linear and Non-linear Analyses of EEG in a Group of ASD Children During Resting State Condition

This study analyses the spontaneous electroencephalogram (EEG) brain activity of 14 children diagnosed with Autism Spectrum Disorder (ASD) compared to 18 children with normal development, aged 5-11 years. (i) Power Spectral Density (PSD), (ii) variability across trials (coefficient of variation: CV), and (iii) complexity (multiscale entropy: MSE) of the brain signal analysis were computed on the resting state EEG. PSD (0.5-45 Hz) and CV were averaged over different frequency bands (low-delta, delta, theta, alpha, low-beta, high-beta and gamma). MSE were calculated with a coarse-grained procedure on 67 time scales and divided into fine, medium and coarse scales. In addition, significant neurophysiological variables were correlated with behavioral performance data (Kaufman Brief Intelligence Test (KBIT) and Autism Spectrum Quotient (AQ)). Results show increased PSD fast frequency bands (high-beta and gamma), higher variability (CV) and lower complexity (MSE) in children with ASD when compared to typically developed children. These results suggest a more variable, less complex and, probably, less adaptive neural networks with less capacity to generate optimal responses in ASD children.

Multiscale entropy of ADHD children during resting state condition

This present study aims to investigate neural mechanisms underlying ADHD compared to healthy children through the analysis of the complexity and the variability of the EEG brain signal using multiscale entropy (MSE), EEG signal standard deviation (SDs), as well as the mean, standard deviation (SDp) and coefficient of variation (CV) of absolute spectral power (PSD). For this purpose, a sample of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) between 6 and 17 years old were selected based on the number of trials and diagnostic agreement, 32 for the open-eyes (OE) experimental condition and 25 children for the close-eyes (CE) experimental condition. Healthy control subjects were age- and gender-matched with the ADHD group. The MSE and SDs of resting-state EEG activity were calculated on 34 time scales using a coarse-grained procedure. In addition, the PSD was averaged in delta, theta, alpha, and beta frequency bands, and its mean, SDp, and CV were calculated. The results show that the MSE changes with age during development, increases as the number of scales increases and has a higher amplitude in controls than in ADHD. The absolute PSD results show CV differences between subjects in low and beta frequency bands, with higher variability values in the ADHD group. All these results suggest an increased EEG variability and reduced complexity in ADHD compared to controls.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09869-0.

Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder

Atypical facial expression is one of the early symptoms of autism spectrum disorder (ASD) characterized by reduced regularity and lack of coordination of facial movements. Automatic quantification of these behaviors can offer novel biomarkers for screening, diagnosis, and treatment monitoring of ASD. In this work, 40 toddlers with ASD and 396 typically developing toddlers were shown developmentally-appropriate and engaging movies presented on a smart tablet during a well-child pediatric visit. The movies consisted of social and non-social dynamic scenes designed to evoke certain behavioral and affective responses. The front-facing camera of the tablet was used to capture the toddlers' face. Facial landmarks' dynamics were then automatically computed using computer vision algorithms. Subsequently, the complexity of the landmarks' dynamics was estimated for the eyebrows and mouth regions using multiscale entropy. Compared to typically developing toddlers, toddlers with ASD showed higher complexity (i.e., less predictability) in these landmarks' dynamics. This complexity in facial dynamics contained novel information not captured by traditional facial affect analyses. These results suggest that computer vision analysis of facial landmark movements is a promising approach for detecting and quantifying early behavioral symptoms associated with ASD.

EEG complexity during mind wandering: A multiscale entropy investigation

Our attention often drifts away from the ongoing task to task-unrelated thoughts, a phenomenon commonly referred to as mind wandering. Ample studies dedicated to delineating its electrophysiological correlates have revealed distinct event-related potentials (ERP) and spectral patterns associated with mind wandering. It remains less clear whether the complexity of the electroencephalography (EEG) changes when our minds wander, a metric that captures the predictability of the time series at varying timescales. Accordingly, this study investigated whether mind wandering impacts EEG signal complexity. We further explored whether such effects differ across timescales, and change in a context-dependent manner as indexed by global and local levels of processing. To address this, we recorded participants' EEG while they completed Navon's global and local processing task and occasionally reported whether they were on-task or mind wandering throughout the task. We found that brain signal complexity as indexed by multiscale entropy decreased at medium timescales in centro-parietal regions and increased at coarse timescales in anterior and posterior regions during mind wandering, as compared to the on-task state, for global processing. Moreover, global processing showed increased complexity at fine to medium timescales compared to local processing. Finally, behavioral performance revealed a context-dependent effect in accuracy measures, with mind wandering showing lower accuracy compared to the on-task state only during the local condition. Taken together, these results indicate that changes in brain signal complexity across timescales may be an important feature of mind wandering.

Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network

BACKGROUND

The demand for early and precise identification of autism spectrum disorder (ASD) presented a challenge to the prediction of ASD with a non-invasive neuroimaging method.

NEW METHOD

A deep learning model was proposed to identify children with ASD using the resting-state functional near-infrared spectroscopy (fNIRS) signals. In this model, the input was the pattern of brain complexity represented by multiscale entropy of fNIRS time-series signals, with the purpose to solve the problem of deep learning analysis when the raw signals were limited by length and the number of subjects. The model consisted of a two-branch deep learning network, where one branch was a convolution neural network and the other was a long short-term memory neural network based on an attention mechanism.

RESULTS

Our model could achieve an identification accuracy of 94%. Further analysis used the SHapley Additive exPlanations (SHAP) method to balance the accuracy and the number of optical channels, thus reducing the complexity of fNIRS experiment. COMPARISON WITH PREVIOUSLY USED METHOD(S): in identification accuracy, our model was about 14% higher than previously used deep learning models with the same input and 4% higher than the same model but directly using fNIRS signals as input. We could obtain a discriminative accuracy of 90% with nearly half of the measurement channels by the SHAP method.

CONCLUSIONS

Using the pattern of brain complexity as input was effective in the deep learning model when the fNIRS signals were insufficient. With the SHAP method, it was possible to reduce the number of optical channels, while maintaining high accuracy in ASD identification.

Brain Complexity Predicts Response to Adrenocorticotropic Hormone in Infantile Epileptic Spasms Syndrome: A Retrospective Study

INTRODUCTION

Infantile epileptic spasms syndrome (IESS) is an age-specific and severe epileptic encephalopathy. Although adrenocorticotropic hormone (ACTH) is currently considered the preferred first-line treatment, it is not always effective and may cause side effects. Therefore, seeking a reliable biomarker to predict the treatment response could benefit clinicians in modifying treatment options.

METHODS

In this study, the complexities of electroencephalogram (EEG) recordings from 15 control subjects and 40 patients with IESS before and after ACTH therapy were retrospectively reviewed using multiscale entropy (MSE). These 40 patients were divided into responders and nonresponders according to their responses to ACTH.

RESULTS

The EEG complexities of the patients with IESS were significantly lower than those of the healthy controls. A favorable response to treatment showed increasing complexity in the γ band but exhibited a reduction in the β/α-frequency band, and again significantly elevated in the δ band, wherein the latter was prominent in the parieto-occipital regions in particular. Greater reduction in complexity was significantly linked with poorer prognosis in general. Occipital EEG complexities in the γ band revealed optimized performance in recognizing response to the treatment, corresponding to the area under the receiver operating characteristic curves as 0.8621, while complexities of the δ band served as a fair predictor of unfavorable outcomes globally.

CONCLUSION

We suggest that optimizing frequency-specific complexities over critical brain regions may be a promising strategy to facilitate predicting treatment response in IESS.

Integration of multiscale entropy and BASED scale of electroencephalography after adrenocorticotropic hormone therapy predict relapse of infantile spasms

BACKGROUND

Even though adrenocorticotropic hormone (ACTH) demonstrated powerful efficacy in the initially successful treatment of infantile spasms (IS), nearly half of patients have experienced a relapse. We sought to investigate whether features of electroencephalogram (EEG) predict relapse in those IS patients without structural brain abnormalities.

METHODS

We retrospectively reviewed data from children with IS who achieved initial response after ACTH treatment, along with EEG recorded within the last two days of treatment. The recurrence of epileptic spasms following treatment was tracked for 12 months. Subjects were categorized as either non-relapse or relapse groups. General clinical and EEG recordings were collected, burden of amplitudes and epileptiform discharges (BASED) score and multiscale entropy (MSE) were carefully explored for cross-group comparisons.

RESULTS

Forty-one patients were enrolled in the study, of which 26 (63.4%) experienced a relapse. The BASED score was significantly higher in the relapse group. MSE in the non-relapse group was significantly lower than the relapse group in the γ band but higher in the lower frequency range (δ, θ, α). Sensitivity and specificity were 85.71% and 92.31%, respectively, when combining MSE in the δ/γ frequency of the occipital region, plus BASED score were used to distinguish relapse from non-relapse groups.

CONCLUSIONS

BASED score and MSE of EEG after ACTH treatment could be used to predict relapse for IS patients without brain structural abnormalities. Patients with BASED score ≥ 3, MSE increased in higher frequency, and decreased in lower frequency had a high risk of relapse.

The automated preprocessing pipe-line for the estimation of scale-wise entropy from EEG data (APPLESEED): Development and validation for use in pediatric populations

It is increasingly understood that moment-to-moment brain signal variability - traditionally modeled out of analyses as mere "noise" - serves a valuable functional role related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) - a measure of signal irregularity across temporal scales - is an increasingly popular analytic technique in human neuroscience calculated from time series such as electroencephalography (EEG) signals. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain's moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in data preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized EEG preprocessing and entropy estimation pipeline that adapts a critical modification to the original MSE algorithm, and generates reliable scale-wise entropy estimates capable of differentiating developmental stages and cognitive states. This novel pipeline - the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED.

A multiple domain postural control assessment in people with Parkinson's disease: traditional, non-linear, and rambling and trembling trajectories analysis

BACKGROUND

Postural impairment is one of the most debilitating symptoms in people with Parkinson's disease (PD), which show faster and more variable oscillation during quiet stance than neurologically healthy individuals. Despite the center of pressure parameters can characterize PD's body sway, they are limited to uncover underlying mechanisms of postural stability and instability.

RESEARCH QUESTION

Do a multiple domain analysis, including postural adaptability and rambling and trembling components, explain underlying postural stability and instability mechanisms in people with PD?

METHOD

Twenty-four individuals (12 people with PD and 12 neurologically healthy peers) performed three 60-s trials of upright quiet standing on a force platform. Traditional and non-linear parameters (Detrended Fluctuation Analysis- DFA and Multiscale Entropy- MSE) and rambling and trembling trajectories were calculated for anterior-posterior (AP) and medial-lateral (ML) directions.

RESULTS

PDG's postural control was worse compared to CG, displaying longer displacement, higher velocity, and RMS. Univariate analyses revealed largely longer displacement and RMS only for the AP direction and largely higher velocity for both AP and ML directions. Also, PD individuals showed lower AP complexity, higher AP and ML DFA, and increased AP and ML displacement, velocity, and RMS of rambling and trembling components compared to neurologically healthy individuals.

SIGNIFICANCE

Based upon these results, people with PD have a lower capacity to adapt posture and impaired both rambling and trembling components compared to neurologically healthy individuals. These findings provide new insights to explain the larger, faster, and more variable sway in people with PD.

Nonlinear Heart Rate Dynamics Before and After Paroxysmal Atrial Fibrillation Events

BACKGROUND

Heart rate complexity, derived from nonlinear heart rate variability (HRV), has been shown to help predict the outcomes of various diseases. Changes in heart rate complexity before and after paroxysmal atrial fibrillation (PAF) events are unclear.

OBJECTIVES

To evaluate changes in heart rate complexity through nonlinear HRV before and after PAF events.

METHODS

We enrolled 65 patients (72 ± 12.34 years old, 31 females) with 99 PAF events who received 24-hour Holter recording, and analyzed nonlinear HRV variables including Poincaré plot analysis, sample entropy (SampEn), and multiscale entropy (MSE). HRV analyses were applied to a 20-minute window before the onset and after the termination of PAF events. HRV parameters were evaluated and compared based on eight different 5-minute time segments, as we divided each 20-minute window into four segments of 5 minutes each.

RESULTS

SampEn and MSE_1~5 significantly decreased before the onset of PAF events, whereas SampEn, MSE_1~5 and MSE_6~20 significantly increased after the termination of PAF events. SD1 and SD2, which are nonlinear HRV parameters calculated via Poincaré plot analysis, did not significantly change before the PAF events, however they both decreased significantly after termination.

CONCLUSIONS

Heart rate complexity significantly decreased before the initiation and increased after the termination of PAF events, which indicates the crucial role of nonlinear heart rate dynamics in the initiation and termination of PAF.

The Multiscale Dynamics of Beat-to-Beat Blood Pressure Fluctuation Mediated the Relationship Between Frailty and Arterial Stiffness in Older Adults

BACKGROUND

Beat-to-beat blood pressure (BP) is an important cardiovascular output and regulated by neurophysiological elements over multiple temporal scales. The multiscale dynamics of beat-to-beat BP fluctuation can be characterized by "BP complexity" and has been linked to age-related adverse health outcomes. We here aimed to examine whether BP complexity mediates the association between arterial stiffness and frailty.

METHOD

This cross-sectional study was completed between January and October 2021. A total of 350 older adults completed assessments for frailty, arterial stiffness (ie, average brachial-ankle pulse wave velocity), and beat-to-beat finger BP. The complexity of beat-to-beat systolic blood pressure (SBP) and diastolic blood pressure (DBP) BP series was measured using multiscale entropy. The relationships between frailty, BP complexity, and arterial stiffness were examined using analysis of variance and linear regression models. The effects of BP complexity on the association between arterial stiffness and frailty were examined using mediation analyses.

RESULTS

Compared with non-frail, prefrail, and frail groups had significantly elevated lower SBP and DBP complexity (F > 11, p < .001) and greater arterial stiffness (F = 16, p < .001). Greater arterial stiffness was associated with lower BP complexity (β < -0.42, p < .001). Beat-to-beat SBP and DBP complexity mediated the association between arterial stiffness and frailty (indirect effects >0.28), accounting for at least 47% of its total effects on frailty (mediated proportion: SBP: 50%, DBP: 47%).

CONCLUSION

This study demonstrates the association between BP complexity and frailty in older adults, and BP complexity mediates the association between arterial stiffness and frailty, suggesting that this metric would serve as a marker to help characterize important functions in the older adults.

Coarse-graining and the Haar wavelet transform for multiscale analysis

BACKGROUND

Multiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation involves first decomposing a signal into multiple sub-signal 'scales' using a coarse-graining algorithm.

METHODS

The coarse-graining algorithm averages adjacent values in a time series to produce a coarser scale time series. The Haar wavelet transform convolutes a time series with a scaled square wave function to produce an approximation which is equivalent to averaging points.

RESULTS

Coarse-graining is mathematically identical to the Haar wavelet transform approximations. Thus, multiscale entropy is entropy computed on sub-signals derived from approximations of the Haar wavelet transform. By describing coarse-graining algorithms properly as Haar wavelet transforms, the meaning of 'scales' as wavelet approximations becomes transparent. The computed value of entropy is different with different wavelet basis functions, suggesting further research is needed to determine optimal methods for computing multiscale entropy.

CONCLUSION

Coarse-graining is mathematically identical to Haar wavelet approximations at power-of-two scales. Referring to coarse-graining as a Haar wavelet transform motivates research into the optimal approach to signal decomposition for entropy analysis.

Effects of various walking intensities on leg muscle fatigue and plantar pressure distributions

Background

Physical activity may benefit health and reduce risk for chronic complications in normal and people with diabetes and peripheral vascular diseases. However, it is unclear whether leg muscle fatigue after weight-bearing physical activities, such as brisk walking, may increase risk for plantar tissue injury. In the literature, there is no evidence on the effect of muscle fatigue on plantar pressure after various walking intensities. The objectives of this study were to investigate the effects of various walking intensities on leg muscle fatigue and plantar pressure patterns.

Methods

A 3 × 2 factorial design, including 3 walking speeds (1.8 (slow and normal walking), 3.6 (brisk walking), and 5.4 (slow running) mph) and 2 walking durations (10 and 20 min) for a total of 6 walking intensities, was tested in 12 healthy participants in 3 consecutive weeks. The median frequency and complexity of electromyographic (EMG) signals of tibialis anterior (TA) and gastrocnemius medialis (GM) were used to quantify muscle fatigue. Fourier transform was used to compute the median frequency and multiscale entropy was used to calculate complexity of EMG signals. Peak plantar pressure (PPP) values at the 4 plantar regions (big toe, first metatarsal head, second metatarsal head, and heel) were calculated.

Results

Two-way ANOVA showed that the walking speed (at 1.8, 3.6, 5.4 mph) significantly affected leg muscle fatigue, and the duration factor (at 10 and 20 min) did not. The one-way ANOVA showed that there were four significant pairwise differences of the median frequency of TA, including walking speed of 1.8 and 3.6 mph (185.7 ± 6.1 vs. 164.9 ± 3.0 Hz, P = 0.006) and 1.8 and 5.4 mph (185.7 ± 6.1 vs. 164.5 ± 5.5 Hz, P = 0.006) for the 10-min duration; and walking speed of 1.8 and 3.6 mph (180.0 ± 5.9 vs. 163.1 ± 4.4 Hz, P = 0.024) and 1.8 and 5.4 mph (180.0 ± 5.9 vs. 162.8 ± 4.9 Hz, P = 0.023) for the 20-min duration. The complexity of TA showed a similar trend with the median frequency of TA. The median frequency of TA has a significant negative correlation with PPP on the big toe ( r = -0.954, P = 0.003) and the first metatarsal head ( r = -0.896, P = 0.016).

Conclusions

This study demonstrated that brisk walking and slow running speeds (3.6 and 5.4 mph) cause an increase in muscle fatigue of TA compared to slow walking speed (1.8 mph); and the increased muscle fatigue is significantly related to a higher PPP.

Complexity analysis of the brain activity in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) due to cognitive loads/demands induced by Aristotle's type of syllogism/reasoning. A Power Spectral Density and multiscale entropy (MSE) analysis

OBJECTIVE

We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders), ADHD (attention-deficit/hyperactivity disorder), compared with healthy subjects during the performance of an innovative cognitive task: Aristotle's valid and invalid syllogisms. We follow the Neuroanatomical differences type of criterion in assessing the results of our study in supporting or not the dual-process theory of Kahneman, 2011) (Systems I & II of thinking).

METHOD

We recorded EEGs from 14 scalp electrodes in 30 adults with ADHD, 30 with ASD and 24 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), the Aristotle's four types of syllogism mentioned above. The multiscale entropy (MSE), a nonlinear information-theoretic measure or tool was computed to extract features that quantify the complexity of the EEG.

RESULTS

The dynamics of the curves of the grand average of MSE values of the ADHD and ASD participants was significantly in higher levels for the majority of time scales, than the healthy subjects over a number of brain regions (electrodes locations), during the performance of both valid and invalid types of syllogism. This result is seemingly not in accordance of the broadly accepted 'theory' of complexity loss in 'pathological' subjects, but actually this is not the case as explained in the text. ADHD subjects are engaged in System II of thinking, for both Valid and Invalid syllogism, ASD and Control in System I for valid and invalid syllogism, respectively. A surprising and 'provocative' result of this paper, as shown in the next sections, is that the Complexity-variability of ASD and ADHD subjects, when they face Aristotle's types of syllogisms, is higher than that of the control subjects. An explanation is suggested as described in the text. Also, in the case of invalid type of Aristotelian syllogisms, the linguistic and visuo-spatial systems are both engaged ONLY in the temporal and occipital regions of the brain, respectively, of ADHD subjects. In the case of valid type, both above systems are engaged in the temporal and occipital regions of the brain, respectively, of both ASD and ADHD subjects, while in the control subjects only the visuo-spatial type is engaged (Goel et al., 2000; Knauff, 2007).

CONCLUSION

Based on the results of the analysis described in this work, the differences in the EEG complexity between the three groups of participants lead to the conclusion that cortical information processing is changed in ASD and ADHD adults, therefore their level of cortical activation may be insufficient to meet the peculiar cognitive demand of Aristotle's reasoning.

SIGNIFICANCE

The present paper suggest that MSE, is a powerful and efficient nonlinear measure in detecting neural dysfunctions in adults with ASD and ADHD characteristics, when they are called on to perform in a very demanding as well as innovative set of cognitive tasks, that can be considered as a new diagnostic 'benchmark' in helping detecting more effectively such type of disorders. A linear measure alone, as the typical PSD, is not capable in making such a distinction. The work contributes in shedding light on the neural mechanisms of syllogism/reasoning of Aristotelian type, as well as toward understanding how humans reason logically and why 'pathological' subjects deviate from the norms of formal logic.

Different effects of essential tremor and Parkinsonian tremor on multiscale dynamics of hand tremor

OBJECTIVE

Essential tremor (ET) and Parkinsonian tremor (PT) are often clinically misdiagnosed due to the overlapping characteristics of their hand tremor. We aim to examine if ET and PT influence the multiscale dynamics of hand tremor, as quantified using complexity, differently, and if such complexity metric is of promise to help identify ET from PT.

METHODS

Forty-eight participants with PT and 48 with ET performed two 30-second tests within each of the following conditions: sitting while resting arms or outstretching arms horizontally. The hand tremor was captured by accelerometers secured to the dorsum of each hand. The complexity was quantified using multiscale entropy.

RESULTS

Compared to PT group, ET group had lower complexity of both hands across conditions (F > 34.2, p < 0.001). Lower complexity was associated with longer disease duration (r² > 0.15, p < 0.009) in both PT and ET, and within PT, greater Unified Parkinson's Disease Rating Scale-III UPDRS-III scores (r² > 0.18, p < 0.009). Receiver-operating-characteristic curves revealed that the complexity metric can distinguish ET from PT (area-under-the-curve > 0.77, cut-off value = 48 (postural), 49 (resting)), which was confirmed in a separate dataset with ET and PT that were clearly diagnosed in prior work.

CONCLUSIONS

The PT and ET have different effects on hand tremor complexity, and this metric is promising to help the identification of ET and PT, which still needs to be confirmed in future studies.

SIGNIFICANCE

The characteristics of multiscale dynamics of the hand tremor, as quantified by complexity, provides novel insights into the different pathophysiology between ET and PT.

Dynamical indicators of resilience from physiological time series in geriatric inpatients: Lessons learned

The concept of physical resilience may help geriatric medicine objectively assess patients' ability to 'bounce back' from future health challenges. Indicators putatively forecasting resilience have been developed under two paradigms with different perspectives: Critical Slowing Down and Loss of Complexity. This study explored whether these indicators validly reflect the construct of resilience in geriatric inpatients. Geriatric patients (n = 121, 60% female) had their heart rate and physical activity continuously monitored using a chest-worn sensor. Indicators from both paradigms were extracted from both physiological signals. Measures of health functioning, concomitant with low resilience, were obtained by questionnaire at admission. The relationships among indicators and their associations with health functioning were assessed by correlation and linear regression analyses, respectively. Greater complexity and higher variance in physical activity were associated with lower frailty (β = -0.28, p = .004 and β = -0.37, p < .001, respectively) and better ADL function (β = 0.23, p = .022 and β = 0.38, p < .001). The associations of physical activity variance with health functioning were not in the expected direction based on Critical Slowing Down. In retrospect, these observations stress the importance of matching the resilience paradigm's assumptions to the homeostatic role of the variable monitored. We present several lessons learned.

Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy

Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6 % , a sensitivity of 96.1 % , and a specificity of 76.9 % . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.

Cortical maturation from childhood to adolescence is reflected in resting state EEG signal complexity

Endogenous cortical fluctuations captured by electroencephalograms (EEGs) reflect activity in large-scale brain networks that exhibit dynamic patterns over multiple time scales. Developmental changes in the coordination and integration of brain function leads to greater complexity in population level neural dynamics. In this study we examined multiscale entropy, a measure of signal complexity, in resting-state EEGs in a large (N = 405) cross-sectional sample of children and adolescents (9–16 years). Our findings showed consistent age-dependent increases in EEG complexity that are distributed across multiple temporal scales and spatial regions. Developmental changes were most robust as the age gap between groups increased, particularly between late childhood and adolescence, and were most prominent over fronto-central scalp regions. These results suggest that the transition from late childhood to adolescence is characterized by age-dependent changes in the underlying complexity of endogenous brain networks.

Remission of depression is associated with asymmetric hemispheric variation in EEG complexity before antidepressant treatment

BACKGROUND

Intense increased alpha activity in the anterior brain has been recognized as one of the biomarkers of depression. This study aimed to explore whether complexity measures of electroencephalogram (EEG) signals vary across the frontal region between patients with or without remission in depression.

METHODS

Ninety-four patients with moderate-to-severe major depression underwent the experiment. Hilbert-Huang transform was applied to extract individual alpha EEG instantaneous amplitude. Averaged regional complexity (SampEn_Mean) on frontal and frontal-central areas as well as bilateral frontal areas were assessed according to multiscale entropy between the remitters (n = 26/94, 27.66%) and poor responders (n = 29/94, 30.85%). Standard deviation of regional complexity (SampEn_SD) was used as a parameter to test the homogeneity of frontal alpha complexity.

RESULTS

No significant differences in SampEn_Mean for the frontal or frontal-central area were observed between the remitters and poor responders. However, the SampEn_SD (i.e., regional homogeneity of frontal alpha complexity) significantly increased in the frontal-central area after treatment among the poor responders in comparison to their baseline, whereas the remitters showed an opposite trend. The remitters further showed significantly larger SampEn_SD on the left frontal area at baseline, and this lateralization disappeared after antidepressant treatment except for the poor responders.

LIMITATIONS

This study was limited by a small sample size and without healthy controls.

CONCLUSIONS

Homogeneity in frontal-central alpha complexity and interhemispheric asymmetry normalization of anterior EEG complexity are associated with antidepressant efficacy, suggesting that homogeneity of dynamical brain activity is a key linked to the successful treatment of major depression.

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time T_R) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.

Electroencephalography complexity in infantile spasms and its association with treatment response

OBJECTIVE

To investigate the potential of EEG multiscale entropy and complexity as biomarkers in infantile spasms.

METHODS

We collected EEG data retrospectively from 16 newly diagnosed patients, 16 age- and gender-matched healthy controls, and 15 drug-resistant patients. The multiscale entropy (MSE) and total EEG complexity before anti-epileptic drug (AED) treatment, before adrenocorticotropic hormone (ACTH) treatment, 14 days after ACTH therapy, and after 6 months of follow-up were calculated.

RESULTS

The total EEG complexity of 16 newly diagnosed infantile spasms patients was lower than the 16 healthy controls (median [IQR]: 351.5 [323.1-388.1] vs 461.6 [407.7-583.4]). The total EEG complexity before treatment was higher in the six patients with good response to AED than the 10 patients without response (median [IQR]: 410.0 [388.1-475.0] vs 344.5 [319.6-352.0]). The total EEG complexity before and after 14-days of ACTH therapy was not different between 13 ACTH therapy responders and nine non-responders. After 6-months follow-up, the total EEG complexity of ACTH therapy responders were higher than non-responders (median [IQR]: 598.5 [517.4-623.3] vs 448.6 [347.1-536.3]).

CONCLUSIONS

The total EEG complexity before AED and 6 months after ACTH are associated with spasm-freedom.

SIGNIFICANCE

The total EEG complexity is a potential biomarker to predict and monitor the treatment effect in infantile spasms.

Heart rhythm complexity as predictors for the prognosis of end-stage renal disease patients undergoing hemodialysis

BACKGROUND

Heart rhythm complexity, a measure of heart rate dynamics and a risk predictor in various clinical diseases, has not been systematically studied in patients with end-stage renal disease. The aim of this study is to investigate the heart rhythm complexity and its prognostic value for mortality in end-stage renal disease patients undergoing hemodialysis.

METHODS

To assess heart rhythm complexity and conventional heart rate variability measures, 4-h continuous electrocardiography for a retrospective cohort of 202 ostensibly healthy control subjects and 51 hemodialysis patients with end-stage renal disease were analyzed. Heart rhythm complexity was quantified by the complexity index from the measurement of the multiscale entropy profile.

RESULTS

During a follow-up of 13 months, 8 people died in the patient group. Values of either traditional heart rate variability measurements or complexity indices were found significantly lower in patients than those in healthy controls. In addition, the complexity indices (Area 1-5, Area 6-15 and Area 6-20) in the mortality group were significantly lower than those in the survival group, while there were no significant differences in traditional heart rate variability parameters between the two groups. In receiver operating characteristic curve analysis, Area 6-20 (AUC = 0.895, p < 0.001) showed the strongest predictive power between mortality and survival groups.

CONCLUSION

The results suggest that heart rhythm complexity is impaired for patients with end-stage renal disease. Furthermore, the complexity index of heart rate variability quantified by multiscale entropy may be a powerful independent predictor of mortality in end-stage renal disease patients undergoing hemodialysis.

Acute particulate matter exposure is associated with disturbances in heart rate complexity in patients with prior myocardial infarction

BACKGROUND

Ambient air pollutants can increase cardiovascular mortality. One possible mechanism is the effect on the autonomic balance of the cardiovascular system. Studies on acute effects of particulate matter (PM) exposure on heart rate variability (HRV), a surrogate marker for autonomic balance, in patients with prior myocardial infarction (MI) revealed inconsistent results.

METHOD

We prospectively enrolled participants with acute MI. These participants received a 24-hour Holter electrocardiography examination and echocardiography six months after the index MI. Linear [standard deviation of all normal to normal intervals, standard deviation of NN intervals (SDNN), and a low-frequency to high-frequency ratio (LF/HF)] and non-linear parameters of heart rate variability [multiscale entropy (MSE)] were calculated to show autonomic balance. Data for PM2.5, PM2.5-10, and PM10, were obtained from a fixed-site station in Taiwan. Linear mixed effect models were used to estimate acute effects (within 0-3 days) of PM exposure (per 10 μg/m3) on heart rate variability.

RESULTS

A total of 90 participants were enrolled in this study with a mean age of 58.7 (13.3) and 83 (92.2%) male participants. Traditional HRV parameters, SDNN and LF/HF, were positively correlated with two-day lagged PM2.5-10 and PM10 [adjusted beta coefficient: SDNN: 130.3 and 58.5; LH/HF: 0.32 and 0.21 (all p < or = 0.01)]. MSE slopes 1-5 were negatively correlated with same-day PM2.5-10 and PM10 (adjusted beta coefficient -0.011 (p = 0.01) and -0.005 (p = 0.02), respectively). The left ventricular ejection fraction was negatively correlated with one-day lagged PM2.5-10, and PM10 (adjusted beta coefficient -0.49 and -0.4, respectively; both p < 0.05), after adjusting for MI size.

CONCLUSION

Our results suggest that coarse PM may acutely affect cardiac autonomic balance. MSE is a sensitive marker for detecting changes in autonomic imbalance in patients with prior MI following acute PM exposure.

Epigenetic tuning of brain signal entropy in emergent human social behavior

BACKGROUND

How the brain develops accurate models of the external world and generates appropriate behavioral responses is a vital question of widespread multidisciplinary interest. It is increasingly understood that brain signal variability-posited to enhance perception, facilitate flexible cognitive representations, and improve behavioral outcomes-plays an important role in neural and cognitive development. The ability to perceive, interpret, and respond to complex and dynamic social information is particularly critical for the development of adaptive learning and behavior. Social perception relies on oxytocin-regulated neural networks that emerge early in development.

METHODS

We tested the hypothesis that individual differences in the endogenous oxytocinergic system early in life may influence social behavioral outcomes by regulating variability in brain signaling during social perception. In study 1, 55 infants provided a saliva sample at 5 months of age for analysis of individual differences in the oxytocinergic system and underwent electroencephalography (EEG) while listening to human vocalizations at 8 months of age for the assessment of brain signal variability. Infant behavior was assessed via parental report. In study 2, 60 infants provided a saliva sample and underwent EEG while viewing faces and objects and listening to human speech and water sounds at 4 months of age. Infant behavior was assessed via parental report and eye tracking.

RESULTS

We show in two independent infant samples that increased brain signal entropy during social perception is in part explained by an epigenetic modification to the oxytocin receptor gene (OXTR) and accounts for significant individual differences in social behavior in the first year of life. These results are measure-, context-, and modality-specific: entropy, not standard deviation, links OXTR methylation and infant behavior; entropy evoked during social perception specifically explains social behavior only; and only entropy evoked during social auditory perception predicts infant vocalization behavior.

CONCLUSIONS

Demonstrating these associations in infancy is critical for elucidating the neurobiological mechanisms accounting for individual differences in cognition and behavior relevant to neurodevelopmental disorders. Our results suggest that an epigenetic modification to the oxytocin receptor gene and brain signal entropy are useful indicators of social development and may hold potential diagnostic, therapeutic, and prognostic value.

Multiscale Dynamics of Spontaneous Brain Activity Is Associated With Walking Speed in Older Adults

BACKGROUND

In older adults, compromised white matter tract integrity within the brain has been linked to impairments in mobility. We contend that poorer integrity disrupts mobility by altering the processing of sensorimotor and cognitive and attentional resources in neural networks. The richness of information processing in a given network can be quantified by calculating the complexity of resting-state functional MRI time series. We hypothesized that (i) older adults with lower brain complexity, specifically within sensorimotor, executive, and attention networks, would exhibit slower walking speed and greater dual-task costs (ie, dual-task cost) and (ii) such complexity would mediate the effect of white matter integrity on these metrics of mobility.

METHODS

Fifty-three older adults completed a walking assessment and a neuroimaging protocol. Brain complexity was quantified by calculating the multiscale entropy of the resting-state functional MRI signal within seven previously defined functional networks. The white matter integrity across structures of the corpus callosum was quantified using fractional anisotropy.

RESULTS

Participants with lower resting-state complexity within the sensorimotor, executive, and attention networks walked more slowly under single- and dual-task (ie, walking while performing a serial-subtraction task) conditions (β > 0.28, p ≤ .01) and had a greater dual-task cost (β < -0.28, p < .04). Complexity in these networks mediated the influence of the corpus callosum genu on both single- (indirect effects > 0.15, 95% confidence intervals = 0.02-0.32) and dual-task walking speeds (indirect effects > 0.13, 95% confidence intervals = 0.02-0.33).

CONCLUSION

These results suggest that the multiscale dynamics of resting-state brain activity correlate with mobility and mediate the effect of the microstructural integrity in the corpus callosum genu on walking speed in older adults.

Dynamic properties of glucose complexity during the course of critical illness: a pilot study

Methods to control the blood glucose (BG) levels of patients in intensive care units (ICU) improve the outcomes. The development of continuous BG levels monitoring devices has also permitted to optimize these processes. Recently it was shown that a complexity loss of the BG signal is linked to poor clinical outcomes. Thus, it becomes essential to decipher this relation to design efficient BG level control methods. In previous studies the BG signal complexity was calculated as a single index for the whole ICU stay. Although, these approaches did not grasp the potential variability of the BG signal complexity. Therefore, we setup this pilot study using a continuous monitoring of central venous BG levels in ten critically ill patients (EIRUS platform, Maquet Critical CARE AB, Solna, Sweden). Data were processed and the complexity was assessed by the detrended fluctuation analysis and multiscale entropy (MSE) methods. Finally, recordings were split into 24 h overlapping intervals and a MSE analysis was applied to each of them. The MSE analysis on time intervals revealed an entropy variation and allowed periodic BG signal complexity assessments. To highlight differences of MSE between each time interval we calculated the MSE complexity index defined as the area under the curve. This new approach could pave the way to future studies exploring new strategies aimed at restoring blood glucose complexity during the ICU stay.

Automated classification of five seizure onset patterns from intracranial electroencephalogram signals

OBJECTIVE

The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patterns reported in the literature, and these patterns have clinical significance. In this work, we propose a system to automatically classify five seizure onset patterns from intracerebral EEG signals.

METHODS

The EEG was segmented by clinicians indicating the start and end time of each seizure onset pattern, the channels involved at onset and the seizure onset pattern. Twelve features that represent the time domain characteristics and signal complexity were extracted from 663 seizures channels of 24 patients. The features were used for classification of the patterns with support vector machine - Error-Correcting Output Codes (SVM-ECOC). Three patient groups with a similar number of seizure segments were created, and one group was used for testing and the rest for training. This test was repeated by rotating the testing and training data.

RESULTS

The feature space formed by both time domain and multiscale sample entropy features perform well in classification of the data. An overall accuracy of 80.7% was obtained with these features and a linear kernel of SVM-ECOC.

CONCLUSIONS

The seizure onset patterns consist of varied time and complexity characteristics. It is possible to automatically classify various seizure onset patterns very similarly to visual classification.

SIGNIFICANCE

The proposed system could aid the medical team in assessing intracerebral EEG by providing an objective classification of seizure onset patterns.

Vestibular training promotes adaptation of multisensory integration in postural control

BACKGROUND

Postural stability depends on the integration of the multisensory system to produce motor outputs. When visual and somatosensory input is reliable, this reduces reliance on the vestibular system. Despite this, vestibular loss can still cause severe postural dysfunction. Training one or more of the three sensory systems through vestibular habituation and adaptation can alter sensory weighting and change postural behavior.

AIM

The purpose of this study was to assess sensory reweighting of postural control processing after combined vestibular activation with voluntary weight shift training in healthy adults.

METHODS

Thirty-three healthy individuals (18-35 y.o.) were randomly assigned to one of three groups: No training (control), visual feedback weight shift training (WST) coupled with an active horizontal headshake (HS) activity to elicit a vestibular perturbation, or the same WST without HS (NoHS). Training was performed 2x/day, every other day (M, W, F), totaling six sessions. Pre- and post- assessments on the Sensory Organization Test (SOT) were performed. Separate between- and within- repeated measures ANOVAs were used to analyze the six SOT equilibrium scores, composite scores, sensory ratios and center of pressure (COP) variables by comparing baseline to post-training. Alpha level was set at p < .05.

RESULTS

There was a significant group x session x condition change (p = .012) in the COP multiscale entropy (MSE) velocity sway in the HS group during SOT conditions 5 and 6. Similarly, COP medio-lateral standard deviation sway (ML Std) showed group x session x visual condition (p = .028), due to HS in condition 6 relative to other two groups.

CONCLUSION

Postural training can alter sensory organization after a visual feedback-vestibular activation training protocol, suggesting a possible sensory reweighting through vestibular adaptation and/or habituation.

SIGNIFICANCE

Translating these findings into a vestibular-impaired population can stimulate the design of a rehabilitation balance protocol.

Complexity based measures of postural stability provide novel evidence of functional decline in fragile X premutation carriers

BACKGROUND

Fragile X Associated Tremor/Ataxia Syndrome (FXTAS) is a neurodegenerative movement disorder characterized by tremor, ataxic gait, and balance issues resulting from a premutation of the Fragile X Mental Retardation 1 (FMR1) gene. No biomarkers have yet been identified to allow early diagnosis of FXTAS, however, recent studies have reported subtle issues in the stability of younger premutation carriers, before disease onset. This study investigates the efficacy of multiscale entropy analysis (MSE) in detecting early changes in the motor system of premutation carriers without FXTAS.

METHODS

Sway complexity of 12 female Premutation carriers and 15 healthy Controls were measured under four conditions: eyes open, closed, and two dual-task conditions. A Sustained Attention Response Task (SART) and a working memory based N-Back task were employed to increase cognitive load while standing on the forceplate. A Complexity Index (Ci) was calculated for anterior-posterior (AP) and mediolateral (ML) sway. Independent t-tests were used to assess between-group differences and Oneway repeated measures ANOVA were used to assess within group differences with Bonferroni corrections to adjust for multiple comparisons.

RESULTS

Group performances were comparable with eyes open and closed conditions. The Carrier group's Ci was consistent across tasks and conditions while the Control group's AP Ci increased significantly during the cognitive dual-task (p = 0.001). There was also a strong correlation between CGG repeat length and complexity for the Carrier group (p = 0.004).

SIGNIFICANCE

Increased sway complexity is believed to stem from reallocation of attention to facilitate the increased cognitive demands of dual-tasks. Carriers' complexity did not change during dual-tasks, possibly indicating capacity interference and inefficient division of attention. Lower sway complexity in carriers suggests diminished adaptive capacity under stress as well as degradation of motor functioning. Therefore, sway complexity may be a useful tool in identifying early functional decline in FMR1 premutation carriers as well as monitoring progression towards disease onset.

Low entropy of interictal gamma oscillations is a biomarker of the seizure onset zone in focal cortical dysplasia type II

OBJECTIVE

Dynamic changes in the regularity of interictal gamma oscillations (GOs, 30-70 Hz) on intracranial electroencephalography (EEG) reflect focal ictogenesis with epileptogenic neuronal synchronization in focal cortical dysplasia (FCD). We investigated whether the regularity of interictal GOs is a biomarker of the seizure onset zone (SOZ) using multiscale entropy analysis.

METHODS

We quantified the regularity of interictal GOs using intracranial EEG data from 1164 electrodes in 13 patients with FCD who were seizure-free postoperatively. The regularity of interictal GOs was quantified as entropy values. Low entropy represents high regularity. We standardized entropy values using Z values for each SOZ, resection area (RA), and the region outside the RA. The cutoff Z values, sensitivity, and specificity for detecting each area were calculated using area under the receiver operating characteristics curves (AUCs).

RESULTS

Low Z values represent higher regularity of GOs. The cutoff Z value of ≤-2.09 for the SOZ had a sensitivity of 100% and specificity of 97.1% (AUC = 0.992 ± 0.002). The cutoff Z value of ≤-0.12 for the RA had a sensitivity of 54.2% and specificity of 73.8% (AUC = 0.673 ± 0.019). The cutoff Z value of ≥-0.11 for the region outside the RA had a sensitivity of 73.8% and specificity of 54.2% (AUC = 0.673 ± 0.019).

CONCLUSIONS

Low entropy of interictal GOs was a reliable biomarker for the SOZ. Maintained high entropy of interictal GOs may be an auxiliary biomarker for nonepileptogenic regions.

SIGNIFICANCE

Low entropy of interictal GOs may be a biomarker for the SOZ in FCD type II.

Assessing spatiotemporal variability of brain spontaneous activity by multiscale entropy and functional connectivity

Brain signaling occurs across a wide range of spatial and temporal scales, and analysis of brain signal variability and synchrony has attracted recent attention as markers of intelligence, cognitive states, and brain disorders. However, current technologies to measure brain signals in humans have limited resolutions either in space or in time and cannot fully capture spatiotemporal variability, leaving it untested whether temporal variability and spatiotemporal synchrony are valid and reliable proxy of spatiotemporal variability in vivo. Here we used optical voltage imaging in mice under anesthesia and wakefulness to monitor cortical voltage activity at both high spatial and temporal resolutions to investigate functional connectivity (FC, a measure of spatiotemporal synchronization), Multi-Scale Entropy (MSE, a measure of temporal variability), and their relationships to Regional Entropy (RE, a measure of spatiotemporal variability). We observed that across cortical space, MSE pattern can largely explain RE pattern at small and large temporal scales with high positive and negative correlation respectively, while FC pattern strongly negatively associated with RE pattern. The time course of FC and small scale MSE tightly followed that of RE, while large scale MSE was more loosely coupled to RE. fMRI and EEG data simulated by reducing spatiotemporal resolution of the voltage imaging data or considering hemodynamics yielded MSE and FC measures that still contained information about RE based on the high resolution voltage imaging data. This suggested that MSE and FC could still be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. Our results support the notion that FC and MSE are effective biomarkers for brain states, and provide a promising viewpoint to unify these two principal domains in human brain data analysis.

Brain Complexity in Children with Mild and Severe Autism Spectrum Disorders: Analysis of Multiscale Entropy in EEG

Multiscale entropy (MSE) model quantifies the complexity of brain functions by measuring the entropy across multiple time-scales. Although MSE model has been applied in children with Autism spectrum disorders (ASD) in previous studies, they were limited to distinguish children with ASD from those normally developed without corresponding severity level of their autistic features. Therefore, we aims  to explore and to identify the MSE features and patterns in children with mild and severe ASD by using a high dense 64-channel array EEG system. This study is a cross-sectional study, where 36 children with ASD were recruited and classified into two groups: mild and severe ASD (18 children in each). Three calculated outcomes identified brain complexity of mild and severe ASD groups: averaged MSE values, MSE topographical cortical representation, and MSE curve plotting. Averaged MSE values of children with mild ASD were higher than averaged MSE value in children with severe ASD in right frontal (0.37 vs. 0.22, respectively, p = 0.022), right parietal (0.31 vs. 0.13, respectively, p = 0.017), left parietal (0.37 vs. 0.17, respectively, p = 0.018), and central cortical area (0.36 vs. 0.21, respectively, p = 0.026). In addition, children with mild ASD showed a clear and more increase in sample entropy values over increasing values of scale factors than children with severe ASD. Obtained data showed different brain complexity (MSE) features, values and topographical representations in children with mild ASD compared with those with severe ASD. As a result of this, MSE could serve as a sensitive method for identifying the severity level of ASD.

Evaluation of postural stability based on a force plate and inertial sensor during static balance measurements

BACKGROUND

Previous research on balance mostly focused on the assessment, training, and improvements of balance through interventions. We investigated tools commonly used to study static balance. Differences in postural stability were analyzed using multiscale entropy (MSE) and feature analysis.

METHODS

A force plate and inertial sensor were used to collect acceleration and center-of-pressure (COP) nonlinear signals. MSE was also used to detect fractal correlations and assess the complexity of univariate data complexity. Fifteen healthy subjects participated in the experiments. Each stood on a force plate and wore a sensor while attempting to maintain postural stability for 30 s in four randomized experiments to evaluate their static balance via a copositive experiment with eyes open/closed and with standing on one foot or both feet. A Wilcoxon-signed rank test was used to confirm that the conditions were significant. Considering the effect of the assessment tools, the influence of the visual and lower limb systems on postural stability was assessed and the results from the inertial sensor and force plate experiments were compared.

RESULTS

Force plate usage provided more accurate readings when completing static balance tasks based on the visual system, whereas an inertial sensor was preferred for lower-limb tasks. Further, the eyes-open-standing-on-one-foot case involved the highest complexity at the X, Y, and Z axes for acceleration and at the ML axis for COP compared with other conditions, from which the axial directions can be identified.

CONCLUSIONS

The findings suggested investigation of different evaluation tool choices that can be easily adapted to suit different needs. The results for the complexity index and traditional balance indicators were comparable in their implications on different conditions. We used MSE to determine the equipment that measures the postural stability performance. We attempted to generalize the applications of complexity index to tasks and training characteristics and explore different tools to obtain different results.

TRIAL REGISTRATION

This study was approved by the Research Ethics Committee of National Taiwan University and classified as expedited on August 24, 2017. The committee is organized under and operates in accordance with Social and Behavioral Research Ethical Principles and Regulations of National Taiwan University and government laws and regulations.

Bi-dimensional multiscale entropy: Relation with discrete Fourier transform and biomedical application

The multiscale entropy (MSE_1D) measure is now widely used to quantify the complexity of time series. The development of complexity measures for images is also a long-standing goal. Recently, the bi-dimensional version of MSE_1D has been proposed (MSE_2D) to analyze images. The interpretation of MSE_2D curves and the applications to real data are still emergent. Because the coarse-graining step in the MSE_2D computation changes the frequency content of the image, we hypothesized a possible dependence between MSE_2D and the discrete Fourier transform (DFT). To analyze this dependence, synthetic as well as biomedical images are analyzed. Our results reveal that i) the profile of MSE_2D is sensitive to both the amplitude and phase of the DFT; ii) MSE_2D could find applications in the biomedical field. This work brings valuable information for MSE_2D interpretation and opens possibilities to study images from an entropy point of view through spatial scales.

The relation between Scrabble expertise and brain aging as measured with EEG brain signal variability

Recent empirical work suggests that the dynamics of brain function, as measured by brain signal variability, differs between younger and older adults. We extended this work by examining how the relationship between brain signal variability and age is altered in the context of expertise. We recorded electroencephalography from Scrabble experts and controls during a visual word recognition task. To measure variability, we used multiscale entropy, which emphasizes the way brain signals behave over a range of timescales and can differentiate the variability of a complex system (the brain) from a purely random system. We replicated previously identified shifts from long-range interactions among neural populations to more local processing in late adulthood. In addition, we demonstrated an age-related increase in midrange neural interactions for experts, suggesting greater maintenance of network integration into late adulthood. Our results indicate that expertise-related differences in the context of age and brain dynamics occur across different timescales and that these differences are linked to task performance.

Chronic vagus nerve stimulation reverses heart rhythm complexity in patients with drug-resistant epilepsy: An assessment with multiscale entropy analysis

OBJECTIVE

Vagus nerve stimulation (VNS) is an adjunctive treatment in drug-resistant epilepsy. The alterations in heart rate dynamics through VNS are not well understood. This study aimed to determine changes in heart rhythm complexity in association with VNS and to relate the findings to the outcome of VNS treatment in patients with drug-resistant epilepsy.

METHODS

We prospectively analyzed 32 patients with drug-resistant epilepsy, who underwent VNS implantation, and 32 age- and sex-matched healthy control subjects. The interictal heartbeat intervals were analyzed using the heart rhythm complexity with multiscale entropy (MSE) and traditional heart rate variability (HRV) analyses based on ambulatory 24-hour electrocardiograms (ECGs).

RESULTS

Patients had significantly decreased complexity indices (Slope 5, Area 1-5, Area 6-15, Area 6-20) on MSE analysis and decreased HRV measurements (standard deviation of the heartbeat interval (SDNN), square root of the mean of sum of squares of the differences between adjacent RR intervals (RMSSD), pNN50, very low frequency (VLF), low frequency (LF), high frequency (HF), total power (TP)) in time and frequency domain analyses. After one year of VNS treatment in patients with drug-resistant epilepsy, there was a trend in an elevated MSE profile with significant higher values between the scales 1 and 9. Vagus nerve stimulation induces a more significant increase of MSE in VNS responders than those in the nonresponders. The conventional HRV measurements did not change.

CONCLUSION

Our results suggest that heart rhythm complexity is impaired in patients with drug-resistant epilepsy, and this is at least partially reversed by VNS treatment. Furthermore, VNS-induced effects on heart rate complexity may be associated with the therapeutic response to VNS in patients with drug-resistant epilepsy.

Complexity of preoperative blood pressure dynamics: possible utility in cardiac surgical risk assessment

Complexity measures are intended to assess the cardiovascular system's capacity to respond to stressors. We sought to determine if decreased BP complexity is associated with increased estimated risk as obtained from two standard instruments: the Society of Thoracic Surgeons' (STS) Risk of Mortality and Morbidity Index and the European System for Cardiac Operative Risk Evaluation Score (EuroSCORE II). In this observational cohort study, preoperative systolic, diastolic, mean (MAP) and pulse pressure (PP) time series were derived in 147 patients undergoing cardiac surgery. The complexity of the fluctuations of these four variables was quantified using multiscale entropy (MSE) analysis. In addition, the traditional time series measures, mean and standard deviation (SD) were also computed. The relationships between time series measures and the risk indices (after logarithmic transformation) were then assessed using nonparametric (Spearman correlation, r_s) and linear regression methods. A one standard deviation change in the complexity of systolic, diastolic and MAP time series was negatively associated (p < 0.05) with the STS and EuroSCORE indices in both unadjusted (21-34%) and models adjusted for age, gender and SD of the BP time series (15-31%). The mean and SD of BP time series were not significantly associated with the risk index except for a positive association with the SD of the diastolic BP. Lower preoperative BP complexity was associated with a higher estimated risk of adverse cardiovascular outcomes and may provide a novel approach to assessing cardiovascular risk. Future studies are needed to determine whether dynamical risk indices can improve current risk prediction tools.

Changes of human movement complexity during maturation: quantitative assessment using multiscale entropy

Movement complexity can be defined as the capability of using different strategies to accomplish a specific task and is expected to increase with maturation, reaching its highest level in adulthood.Multiscale Entropy (MSE) has been proposed to estimate complexity on different kinematic signals, at different time scales. When applied on trunk acceleration data during natural walking (NW) at different ages, MSE decreased from childhood to adulthood, apparently contradicting the premises. On the contrary, authors hypothesised that this decrease was dependent on the specific task analysed and resulted from the concurrent increase in gait automaticity.This work aims to test this hypothesis, applying MSE on a non-paradigmatic task (tandem walking, TW), in order to exclude aspects related to automaticity.MSE was estimated on trunk acceleration data, collected on children, adolescents, and young adults during TW and NW. As hypothesized, MSE increased significantly with age in TW and decreased in NW on the sagittal plane. Assuming the development of complexity in TW as reference, MSE in NW showed a reduction to half of the complexity of TW with maturation on the sagittal plane. These results indicate MSE as sensitive to differences in performance due to maturation and to expected changes in complexity related to the specific performed task.

Impairment of heart rhythm complexity in patients with drug-resistant epilepsy: An assessment with multiscale entropy analysis

OBJECTIVE

Epilepsy and seizures can have dramatic effects on the cardiac function. The aim of this study was to investigate the heart rhythm complexity in patients with drug-resistant epilepsy (DRE).

METHODS

Ambulatory 24-h electrocardiograms (ECG) from 70 DRE patients and 50 healthy control subjects were analyzed using conventional heart rate variability (HRV) and multiscale entropy (MSE) methods The variation of complexity indices (CI), which was calculated from MSE profile, was determined.

RESULTS

DRE patients had significantly lower time domain (Mean RR, SDNN, RMSSD, pNN50) and frequency domain (VLF, LF, HF, TP) HRV measurements than healthy controls. Of the MSE analysis, MSE profile, CI including Slope 5, Area 1-5, Area 6-15 and Area 6-20 were significantly lower than those in the healthy control group. In receiver operating characteristic (ROC) curve analysis, VLF had the greatest discriminatory power for the two groups. In both net reclassification improvement (NRI) model and integrated discrimination improvement (IDI) models, CI derived from MSE profiles significantly improved the discriminatory power of Mean RR, SDNN, RMSSD, pNN50, VLF, LF, HF and TP.

SIGNIFICANCE

The heart rate complexity is impaired for DRE patients. CI are useful to discriminate DRE patients from subjects with normal cardiac complexity. These findings indicate that MSE method may serve as a complementary approach for characterizing and understanding abnormal heart rate dynamics in epilepsy. Furthermore, the CI may potentially be used as a biomarker in monitoring epilepsy.

Bilinguals have more complex EEG brain signals in occipital regions than monolinguals

Brain signal complexity increases with development and is associated with better cognitive outcomes in older age. Research has also shown that bilinguals are able to stave off cognitive decline for longer periods of time than monolinguals, but no studies to date have examined whether bilinguals have more complex brain signals than monolinguals. Here we explored the hypothesis that bilingualism leads to greater brain signal complexity by examining multiscale entropy (MSE) in monolingual and bilingual young adults while EEG was recorded during a task-switching paradigm. Results revealed that bilinguals had greater brain signal complexity than monolinguals in occipital regions. Furthermore, bilinguals performed better with increasing occipital brain signal complexity, whereas monolinguals relied on coupling with frontal regions to demonstrate gains in performance. These findings are discussed in terms of how a lifetime of experience with a second language leads to more automatic and efficient processing of stimuli and how these adaptations could contribute to the prevention of cognitive decline in older age.

Intra- and inter-session reliability of traditional and entropy-based variables describing stance on a wobble board

A wobble board (WB) is a balance rehabilitation tool that is used in physiotherapy to improve strength and stability. The WB tested in this study includes a sensory module for measuring patients' tilt and rotation during stance. The aim of this study was to assess the reliability and validity of a balance measurement using a WB. Thirty healthy young adults participated in this study. The participants stood on the WB to simultaneously record the tilt of the WB and the center of pressure data using a force plate. The data were recorded during five measurement sessions on various days, with four trials each. Sways, velocities and indexes of complexity (CI) were computed. For reliability assessment, we used intra-class correlation coefficients within and between sessions; for validity, we computed Spearman correlation coefficients. The velocities and CI showed good intra-session reliability, and the sways showed mostly poor intra-session reliability. The results of inter-session reliability showed good to excellent reliability for CI, poor reliability for sways and poor to good reliability for velocities. The Spearman correlation coefficient showed excellent agreement between the mean velocities computed from the force plate and the WB. Our results confirm that the WB tested is suitable for stability assessment in young adults.

Continuous Monitoring of the Complexity of Intracranial Pressure After Head Injury

Multiscale entropy (MSE) has been increasingly used to investigate the complexity of biological signals. Our previous study demonstrated that the complexity of mean intracranial pressure (ICP), assessed by MSE based on the whole recording periods, is associated with the outcome after traumatic brain injury (TBI). To improve the feasibility of MSE in a clinical setting, this study examined whether the complexity of ICP waveforms based on shorter periods could be a reliable predictor of the outcome in patients with TBI. Results showed that the complexity of ICP slow waves, calculated in 3-h moving windows, correlates with the outcome of patients with TBI. Thus, the complexity of ICP may be a promising index to be incorporated into multimodal monitoring in patients with TBI.

Neural complexity as a potential translational biomarker for psychosis

BACKGROUND

The adaptability of the human brain to the constantly changing environment is reduced in patients with psychotic disorders, leading to impaired cognitive functions. Brain signal complexity, which may reflect adaptability, can be readily quantified via resting-state functional magnetic resonance imaging (fMRI) signals. We hypothesized that resting-state brain signal complexity is altered in psychotic disorders, and is correlated with cognitive impairment.

METHODS

We assessed 156 healthy controls (HC) and 330 probands, including 125 patients with psychotic bipolar disorder (BP), 107 patients with schizophrenia (SZ), 98 patients with schizoaffective disorder (SAD) and 230 of their unaffected first-degree relatives (76 BPR, 79 SADR, and 75 SZR) from four sites of the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Using multi-scale entropy analysis, we determined whether patients and/or relatives had pathologic differences in complexity of resting-state fMRI signals toward regularity (reduced entropy in all time scales), or toward uncorrelated randomness (increased entropy in fine time scales that decays as the time scale increases) and how these complexity differences might be associated with cognitive impairment.

RESULTS

Compared to HC subjects, proband groups showed either decreased complexity toward regularity or toward randomness. SZ probands showed decreased complexity toward regular signal in hypothalamus, and BP probands in left inferior occipital, right precentral and left superior parietal regions, whereas no brain region with decreased complexity toward regularity was found in SAD probands. All proband groups showed significantly increased brain signal randomness in dorsal and ventral prefrontal cortex (PFC), and unaffected relatives showed no complexity differences in PFC regions. SZ had the largest area of involvement in both dorsal and ventral PFC. BP and SAD probands shared increased brain signal randomness in ventral medial PFC, BP and SZ probands shared increased brain signal randomness in ventral lateral PFC, whereas SAD and SZ probands shared increased brain signal randomness in dorsal medial PFC. Only SZ showed increased brain signal randomness in dorsal lateral PFC. The increased brain signal randomness in dorsal or ventral PFC was weakly associated with reduced cognitive performance in psychotic probands.

CONCLUSION

These observations support the loss of brain complexity hypothesis in psychotic probands. Furthermore, we found significant differences as well as overlaps of pathologic brain signal complexity between psychotic probands by DSM diagnoses, thus suggesting a biological approach to categorizing psychosis based on functional neuroimaging data.

Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.e., variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFE_σ) and mean (RCMFE_μ) to quantify the dynamical properties of spread and mean, respectively, over multiple time scales. We demonstrate the dependency of the RCMFE_σ and RCMFE_μ, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. The results evidenced that the RCMFE_σ and RCMFE_μ values are more stable and reliable than the classical multiscale entropy ones. We also inspect the ability of using the standard deviation as well as the mean in the coarse-graining process using magnetoencephalograms in Alzheimer’s disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicated that when the RCMFE_μ cannot distinguish different types of dynamics of a particular time series at some scale factors, the RCMFE_σ may do so, and vice versa. The results showed that RCMFE_σ-based features lead to higher classification accuracies in comparison with the RCMFE_μ-based ones. We also made freely available all the Matlab codes used in this study at 10.7488/ds/1477.

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.

Charting moment-to-moment brain signal variability from early to late childhood

Large-scale brain signals exhibit rich intermittent patterning, reflecting the fact that the cortex actively eschews fixed points in favor of itinerant wandering with frequent state transitions. Fluctuations in endogenous cortical activity occur at multiple time scales and index a dynamic repertoire of network states that are continuously explored, even in the absence of external sensory inputs. Here, we quantified such moment-to-moment brain signal variability at rest in a large, cross-sectional sample of children ranging in age from seven to eleven years. Our findings revealed a monotonic rise in the complexity of electroencephalogram (EEG) signals as measured by sample entropy, from the youngest to the oldest age cohort, across a range of time scales and spatial regions. From year to year, the greatest changes in intraindividual brain signal variability were recorded at electrodes covering the anterior cortical zones. These results provide converging evidence concerning the age-dependent expansion of functional cortical network states during a critical developmental period ranging from early to late childhood.

Complexity of human gait pattern at different ages assessed using multiscale entropy: From development to decline

Multiscale entropy (MSE) has been applied in biomechanics to evaluate gait stability during human gait and was found to be a promising method for evaluating fall risk in elderly and/or pathologic subjects. The hypothesis of this work is that gait complexity is a relevant parameter of gait development during life, decreasing from immature to mature gait and then increasing again during old age. In order to verify this hypothesis, MSE was applied on trunk acceleration data collected during gait of subjects of different ages: toddlers at the onset of walking, pre-scholar and scholar children, adolescents, young adults, adults and elderlies. MSE was estimated by calculating sample entropy (SEN) on raw unfiltered data of L5 acceleration along the three axes, using values of τ ranging from 1 to 6. In addition, other performance parameters (cadence, stride time variability and harmonic ratio) were evaluated. The results followed the hypothesized trend when MSE was applied on the vertical and/or anteroposterior axis of trunk acceleration: an age effect was found and adult SEN values were significantly different from children ones. From young adults to elderlies a slight increase in SEN values was shown although not statistically significant. While performance gait parameters showed adolescent gait similar to the one of adults, SEN highlighted that their gait maturation is not complete yet. In conclusion, present results suggest that the complexity of gait, evaluated on the sagittal plane, can be used as a characterizing parameter of the maturation of gait control.